From c6a8b2887e967494d9f6b1006769018c26810494 Mon Sep 17 00:00:00 2001 From: 20201111139 <2968492110@qq.com> Date: Tue, 11 Feb 2025 15:40:14 +0800 Subject: [PATCH] [benchmark] Add Omnidocbench --- vlmeval/dataset/Omnidocbench/test.ipynb | 65 -- vlmeval/dataset/Omnidocbench/test.py | 807 ------------------------ 2 files changed, 872 deletions(-) delete mode 100644 vlmeval/dataset/Omnidocbench/test.ipynb delete mode 100644 vlmeval/dataset/Omnidocbench/test.py diff --git a/vlmeval/dataset/Omnidocbench/test.ipynb b/vlmeval/dataset/Omnidocbench/test.ipynb deleted file mode 100644 index b3f23060..00000000 --- a/vlmeval/dataset/Omnidocbench/test.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'torch'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_54291/342573136.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0momnidocbench\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOmnidocbenchend2endEvaluator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0meval_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/mnt/petrelfs/wangfangdong/wang/VLMEvalKit/outputs/Qwen2-VL-2B-Instruct/Qwen2-VL-2B-Instruct_OmniDocBench.xlsx'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtsv_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/mnt/petrelfs/wangfangdong/LMUData/OmniDocBench.tsv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/wang/VLMEvalKit/vlmeval/dataset/Omnidocbench/omnidocbench.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtempfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbase64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_base\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImageBaseDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0msmp\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'" - ] - } - ], - "source": [ - "from omnidocbench import Omnidocbenchend2endEvaluator\n", - "\n", - "eval_file='/mnt/petrelfs/wangfangdong/wang/VLMEvalKit/outputs/Qwen2-VL-2B-Instruct/Qwen2-VL-2B-Instruct_OmniDocBench.xlsx'\n", - "tsv_path='/mnt/petrelfs/wangfangdong/LMUData/OmniDocBench.tsv'\n", - "\n", - "eval=Omnidocbenchend2endEvaluator(eval_file,tsv_path)\n", - "result=eval.score()\n", - "print(result)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluator=Omnidocbenchend2endEvaluator(eval_file,tsv_path)\n", - "result=evaluator.score()\n", - "result" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "omni", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.20" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/vlmeval/dataset/Omnidocbench/test.py b/vlmeval/dataset/Omnidocbench/test.py deleted file mode 100644 index 186e01df..00000000 --- a/vlmeval/dataset/Omnidocbench/test.py +++ /dev/null @@ -1,807 +0,0 @@ -import json -import os -import copy -import pandas as pd -from tqdm import tqdm -import Levenshtein -import tempfile -from collections import deque -import base64 -from collections import defaultdict -import numpy as np -from lxml import etree, html -from apted import APTED, Config -from apted.helpers import Tree -from lxml import etree, html - -from utils import match_gt2pred_simple, match_gt2pred_no_split,match_gt2pred_quick,md_tex_filter -from metrics import show_result, get_full_labels_results, get_page_split -from metrics import METRIC_REGISTRY,recogition_end2end_base_dataset,recogition_end2end_table_dataset - -from func_timeout import FunctionTimedOut, func_timeout - - - -eval_file='/mnt/petrelfs/wangfangdong/wang/VLMEvalKit/outputs/Qwen2-VL-2B-Instruct/Qwen2-VL-2B-Instruct_OmniDocBench.xlsx' -tsv_path='/mnt/petrelfs/wangfangdong/LMUData/OmniDocBench.tsv' - -class TableTree(Tree): - def __init__(self, tag, colspan=None, rowspan=None, content=None, *children): - self.tag = tag - self.colspan = colspan - self.rowspan = rowspan - self.content = content - self.children = list(children) - - def bracket(self): - """Show tree using brackets notation""" - if self.tag == 'td': - result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \ - (self.tag, self.colspan, self.rowspan, self.content) - else: - result = '"tag": %s' % self.tag - for child in self.children: - result += child.bracket() - return "{{{}}}".format(result) - - -class CustomConfig(Config): - @staticmethod - def maximum(*sequences): - """Get maximum possible value - """ - return max(map(len, sequences)) - - def normalized_distance(self, *sequences): - """Get distance from 0 to 1 - """ - return float(Levenshtein.distance(*sequences)) / self.maximum(*sequences) - - def rename(self, node1, node2): - """Compares attributes of trees""" - if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): - return 1. - if node1.tag == 'td': - if node1.content or node2.content: - return self.normalized_distance(node1.content, node2.content) - return 0. - - - -class call_TEDS(): - def __init__(self, samples): - self.samples = samples - # type(samples): - print(f'type(samples):{type(samples)}') #dict {{},{},{}} - print(samples) - - - """ - {'gt_idx': [0], - 'gt': '\n\n\n \n \n\n\n\n\n \n \n\n\n
得分
阅卷人
', - 'pred_idx': [''], 'pred': '', 'gt_position': [10], - 'pred_position': '', - 'norm_gt': '
得分
阅卷人
', - 'norm_pred': '', - 'gt_category_type': 'table', - 'pred_category_type': '', - 'gt_attribute': [{'table_layout': 'horizontal', - 'with_span': False, 'line': 'full_line', 'language': 'table_simplified_chinese', 'include_equation': False, 'include_photo': False, 'include_background': False, 'with_structured_text': False}], 'edit': 1, - 'img_id': 'jiaocaineedrop_38247658.pdf_0.jpg'} - """ - def evaluate(self, group_info=[], save_name='default'): - - teds = TEDS(structure_only=False) - teds_structure_only = TEDS(structure_only=True) - - group_scores = defaultdict(list) - group_scores_structure_only = defaultdict(list) - samples = self.samples - - for i,sample in enumerate(samples): - - gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt'] - pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred'] - - score = teds.evaluate(pred, gt) - score_structure_only = teds_structure_only.evaluate(pred, gt) - - if i==79 or i==145 or i==376 or i==377: - print(f'第{i}个元素有得分') - print('TEDS score:', score) - # print(sample) - # print(f'pred:{pred}') - # print(f'gt:{gt}') - - - - group_scores['all'].append(score) - group_scores_structure_only['all'].append(score_structure_only) - if not sample.get('metric'): - sample['metric'] = {} - sample['metric']['TEDS'] = score - sample['metric']['TEDS_structure_only'] = score_structure_only - for group in group_info: - select_flag = True - for k, v in group.items(): - for gt_attribute in sample['gt_attribute']: # gt_attribute is a list containing all merged gt attributes - if not gt_attribute: # if no GT attributes, don't include in calculation - select_flag = False - elif gt_attribute[k] != v: # if any gt attribute doesn't meet criteria, don't select - select_flag = False - if select_flag: - group_scores[str(group)].append(score) - - - - result = {} - for group_name, scores in group_scores.items(): - if len(scores) > 0: - result[group_name] = sum(scores) / len(scores) # average of normalized scores at sample level - else: - result[group_name] = 'NaN' - print(f'Warning: Empyty matched samples for {group_name}.') - - structure_only_result = {} - for group_name, scores in group_scores_structure_only.items(): - if len(scores) > 0: - structure_only_result[group_name] = sum(scores) / len(scores) # average of normalized scores at sample level - else: - structure_only_result[group_name] = 'NaN' - print(f'Warning: Empyty matched samples for {group_name}.') - - return samples, {'TEDS': result, 'TEDS_structure_only': structure_only_result} - - -class TEDS(object): - ''' Tree Edit Distance basead Similarity - ''' - def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None): - assert isinstance(n_jobs, int) and (n_jobs >= 1), 'n_jobs must be an integer greather than 1' - self.structure_only = structure_only - self.n_jobs = n_jobs - self.ignore_nodes = ignore_nodes - self.__tokens__ = [] - - def tokenize(self, node): - ''' Tokenizes table cells - ''' - self.__tokens__.append('<%s>' % node.tag) - if node.text is not None: - self.__tokens__ += list(node.text) - for n in node.getchildren(): - self.tokenize(n) - if node.tag != 'unk': - self.__tokens__.append('' % node.tag) - if node.tag != 'td' and node.tail is not None: - self.__tokens__ += list(node.tail) - - def load_html_tree(self, node, parent=None): - ''' Converts HTML tree to the format required by apted - ''' - global __tokens__ - if node.tag == 'td': - if self.structure_only: - cell = [] - else: - self.__tokens__ = [] - self.tokenize(node) - cell = self.__tokens__[1:-1].copy() - new_node = TableTree(node.tag, - int(node.attrib.get('colspan', '1')), - int(node.attrib.get('rowspan', '1')), - cell, *deque()) - else: - new_node = TableTree(node.tag, None, None, None, *deque()) - if parent is not None: - parent.children.append(new_node) - if node.tag != 'td': - for n in node.getchildren(): - self.load_html_tree(n, new_node) - if parent is None: - return new_node - - def evaluate(self, pred, true): - ''' Computes TEDS score between the prediction and the ground truth of a - given sample - ''' - if (not pred) or (not true): - return 0.0 - parser = html.HTMLParser(remove_comments=True, encoding='utf-8') - pred = html.fromstring(pred, parser=parser) - true = html.fromstring(true, parser=parser) - if pred.xpath('body/table') and true.xpath('body/table'): - pred = pred.xpath('body/table')[0] - true = true.xpath('body/table')[0] - if self.ignore_nodes: - etree.strip_tags(pred, *self.ignore_nodes) - etree.strip_tags(true, *self.ignore_nodes) - n_nodes_pred = len(pred.xpath(".//*")) - n_nodes_true = len(true.xpath(".//*")) - n_nodes = max(n_nodes_pred, n_nodes_true) - tree_pred = self.load_html_tree(pred) - tree_true = self.load_html_tree(true) - distance = APTED(tree_pred, tree_true, CustomConfig()).compute_edit_distance() - return 1.0 - (float(distance) / n_nodes) - else: - return 0.0 - - def batch_evaluate(self, pred_json, true_json): - ''' Computes TEDS score between the prediction and the ground truth of - a batch of samples - @params pred_json: {'FILENAME': 'HTML CODE', ...} - @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...} - @output: {'FILENAME': 'TEDS SCORE', ...} - ''' - samples = true_json.keys() - # if self.n_jobs == 1: - scores = [self.evaluate(pred_json.get(filename, ''), true_json[filename]['html']) for filename in tqdm(samples)] - # else: - # inputs = [{'pred': pred_json.get(filename, ''), 'true': true_json[filename]['html']} for filename in samples] - # scores = parallel_process(inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1) - scores = dict(zip(samples, scores)) - return scores - - - -class Omnidocbenchend2endEvaluator(): - def __init__(self, - eval_file, - tsv_path, - match_method:str='quick_match', - filter_types:dict=None): - - self.eval_file=eval_file - self.match_method=match_method - self.references=[] - self.predictions = pd.read_excel(eval_file)['prediction'].tolist() - self.dafault_metircs_dict={ - 'text_block': - {'metric': ['Edit_dist', 'BLEU', 'METEOR']}, - 'display_formula': - {'metric': ['Edit_dist', 'CDM']}, - 'table': - {'metric': ['TEDS', 'Edit_dist']}, - 'reading_order': - {'metric': ['Edit_dist']} - } - - references = pd.read_csv(tsv_path, sep='\t')['answer'].tolist() - - load_success,load_fail=0,0 - # str->dict - for i,ans in enumerate(references): - try: - ans = json.loads(ans) - load_success+=1 - self.references.append(ans) #[{},{}] - except json.JSONDecodeError as e: - load_fail+=1 - continue - print(f'load_success:{load_success},load_fail:{load_fail}') - - filtered_gt_samples = [] - if filter_types: - for gt_sample in self.references: - select_flag = True - for k, v in filter_types.items(): - if gt_sample["page_info"]["page_attribute"][k] != v: - select_flag = False - if select_flag: - filtered_gt_samples.append(gt_sample) - else: - filtered_gt_samples = self.references #[{},{},{}] - self.references=filtered_gt_samples - - - def score(self)->dict: - samples=self.get_matched_elements(self.references,self.predictions) - metrics=self.process_generated_metric_results(samples) - return metrics - - def get_page_elements(self, selected_annos): - saved_element_dict = defaultdict(list) - related_truncated = [] - truncated_all = {} - for relation in selected_annos["extra"]["relation"]: # Handle truncated text issues - if relation["relation_type"] == 'truncated': - truncated_all[relation["source_anno_id"]] = "" - truncated_all[relation["target_anno_id"]] = "" - exist_flag = False - for merge_list in related_truncated: - if relation["source_anno_id"] in merge_list or relation["target_anno_id"] in merge_list: # Consider cases where three text blocks may need to be merged - merge_list.append(relation["source_anno_id"]) - merge_list.append(relation["target_anno_id"]) - exist_flag = True - if not exist_flag: - related_truncated.append([relation["source_anno_id"], relation["target_anno_id"]]) - - for item in selected_annos['layout_dets']: - if item['anno_id'] not in truncated_all.keys(): - saved_element_dict[item["category_type"]].append(item) - else: - truncated_all[item['anno_id']] = item - - for merge_list in related_truncated: - text_block_list = [truncated_all[key] for key in merge_list] - sorted_block = sorted(text_block_list, key=lambda x: x['order']) - text = "" - for block in sorted_block: - text += block['text'] - merged_block = { - "category_type": sorted_block[0]["category_type"], # Directly use information from the first block - "order": sorted_block[0]["order"], - "anno_id": sorted_block[0]["anno_id"], - "text": text, - "merge_list": sorted_block - } - saved_element_dict[sorted_block[0]["category_type"]].append(merged_block) - - return saved_element_dict - - def get_page_elements_list(self, gt_page_elements, category_list): - element_list = [] - for category_type in category_list: - if gt_page_elements.get(category_type): - element_list.extend(gt_page_elements[category_type]) - return element_list - - def get_sorted_text_list(self, selected_annos): - # txt_type: text, latex, html - text_list = [] - for item in selected_annos: - if item.get('order'): - order = item['order'] - else: - order = 0 - # 【txt_type,selecte_annos] - text_list.append((order, item)) - sorted_text_list = sorted(text_list, key=lambda x: x[0]) - return [_[1] for _ in sorted_text_list] - - def filtered_out_ignore(self, items, ignore_category_list): - filted_items = [] - for item in items: - if item['gt_category_type'] not in ignore_category_list: - filted_items.append(item) - return filted_items - - def get_order_paired(self, order_match_s, img_name): - matched = [(item['gt_position'], item['pred_position']) for item in order_match_s if (item['gt_position'] != [""] and item['pred_position'] != "")] - gt_idx_all = [item['gt_position'] for item in order_match_s if (item['gt_position'] != [""])] - read_order_pred = [i[0] for i in sorted(matched, key=lambda x: x[1])] - read_order_gt = sum(gt_idx_all, []) # Convert to one-dimensional list - read_order_gt = [x for x in read_order_gt if x] - gt = sorted(read_order_gt) - pred = sum(read_order_pred, []) - pred = [x for x in pred if x] - if len(pred) > 0 or len(gt) > 0: - edit = Levenshtein.distance(gt, pred)/ max(len(pred), len(gt)) - return { - 'gt': gt, - 'pred': pred, - 'img_id': img_name, - 'edit': edit - } - else: - return {} # If both GT and pred are empty for the page, return empty - - def formula_format(self, formula_matches, img_name): - # formated_list = [] - for i, item in enumerate(formula_matches): - item["img_id"] = img_name + '_' + str(i) - return formula_matches - - def get_matched_elements(self,references:list,predictions:list)->dict: - - plain_text_match = [] - display_formula_match = [] - html_table_match = [] - latex_table_match = [] - order_match = [] - - - for i,sample in enumerate(references): - img_name = os.path.basename(sample["page_info"]["image_path"]) - pred_content = predictions[i] - result = self.process_get_matched_elements(sample, pred_content, img_name) - [plain_text_match_clean, formated_display_formula, latex_table_match_s, html_table_match_s, order_match_single] = result - - if order_match_single: - order_match.append(order_match_single) - if plain_text_match_clean: - plain_text_match.extend(plain_text_match_clean) - if formated_display_formula: - display_formula_match.extend(formated_display_formula) - if latex_table_match_s: - latex_table_match.extend(latex_table_match_s) - if html_table_match_s: - html_table_match.extend(html_table_match_s) - - if len(latex_table_match) > len(html_table_match): - table_match = latex_table_match - table_format = 'latex' - else: - table_match = html_table_match - table_format = 'html' - - matched_samples_all = { - 'text_block': recogition_end2end_base_dataset(plain_text_match), - 'display_formula': recogition_end2end_base_dataset(display_formula_match), - 'table': recogition_end2end_table_dataset(table_match, table_format), - 'reading_order': recogition_end2end_base_dataset(order_match) - } - - return matched_samples_all - - def process_get_matched_elements(self, sample, pred_content, img_name): - if self.match_method == 'simple_match': # add match choice - match_gt2pred = match_gt2pred_simple - elif self.match_method == 'quick_match': - match_gt2pred = match_gt2pred_quick - elif self.match_method == 'no_split': - match_gt2pred = match_gt2pred_no_split - else: - # print('Invalid match method name. The quick_match will be used.') - match_gt2pred = match_gt2pred_quick - - pred_dataset = md_tex_filter(pred_content) - gt_page_elements = self.get_page_elements(sample) - - text_all = self.get_page_elements_list(gt_page_elements, ['text_block', 'title', 'code_txt', 'code_txt_caption', 'reference', 'equation_caption', - 'figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', - 'header', 'footer', 'page_footnote', 'page_number']) - - - display_formula_match_s = [] - plain_text_match_clean = [] - latex_table_match_s = [] - html_table_match_s = [] - order_match_single = [] - if text_all: - gt_text_list = self.get_sorted_text_list(text_all) - try: - plain_text_match_s = func_timeout( - 30, match_gt2pred, args=(gt_text_list, pred_dataset['text_all'], 'text', img_name) - ) - except FunctionTimedOut as e1: - print(f'Time out for plain text match of {img_name}, match_gt2pred_simple will be used.') - plain_text_match_s = match_gt2pred_simple(gt_text_list, pred_dataset['text_all'], 'text', img_name) - except Exception as e: - print(str(e)) - sys.exit() - - if not plain_text_match_s: - print(f'No text match of {img_name}. The plain text match will be empty.') - else: - plain_text_match_clean = self.filtered_out_ignore(plain_text_match_s, ['figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number', 'equation_caption']) - - - if gt_page_elements.get('equation_isolated'): - gt_display_list = self.get_sorted_text_list(gt_page_elements['equation_isolated']) - display_formula_match_s = match_gt2pred(gt_display_list, pred_dataset['equation_isolated'], 'formula', img_name) - display_formula_match_s = [x for x in display_formula_match_s if x['gt_idx'] != [""]] - if not display_formula_match_s: - print(f'No display_formula_match of {img_name}. The display_formula_match will be empty.') - - if gt_page_elements.get('table'): - gt_table_list = self.get_sorted_text_list(gt_page_elements['table']) - if pred_dataset['latex_table']: - latex_table_match_s = match_gt2pred_simple(gt_table_list, pred_dataset['latex_table'], 'latex_table', img_name) - latex_table_match_s = [x for x in latex_table_match_s if x['gt_idx'] != [""]] - if pred_dataset['html_table']: - html_table_match_s = match_gt2pred_simple(gt_table_list, pred_dataset['html_table'], 'html_table', img_name) - html_table_match_s = [x for x in html_table_match_s if x['gt_idx'] != [""]] - else: - html_table_match_s = match_gt2pred_simple(gt_table_list, [], 'html_table', img_name) - html_table_match_s = [x for x in html_table_match_s if x['gt_idx'] != [""]] - - - order_match_s = plain_text_match_clean - if order_match_s: - order_match_single = self.get_order_paired(order_match_s, img_name) - - return [plain_text_match_clean, display_formula_match_s, latex_table_match_s, html_table_match_s, order_match_single] - - - def process_generated_metric_results(self,samples,save_name:str='end2end_quick_match'): - - result_all={} - page_info={} - metircs_dict=self.dafault_metircs_dict - pages=self.references #gt_samples list - - for page in pages: - img_path=os.path.basename(page['page_info']['image_path']) - page_info[img_path]=page['page_info']['page_attribute'] - - for element in metircs_dict.keys(): - result={} - group_info=metircs_dict[element].get('group',[]) - sample = samples.get(element) - - for metric in metircs_dict[element]['metric']: - metric_val = METRIC_REGISTRY.get(metric) - - sample,result_s = metric_val(sample).evaluate(group_info, f"{save_name}_{element}") - if result_s: - result.update(result_s) - # if isinstance(result_s, tuple) and len(result_s) > 1 and isinstance(result_s[1], dict): - # result.update(result_s[1]) - if result: - print(f"{element}") - show_result(result) - result_all[element]={} - - - group_result=get_full_labels_results(sample) - page_result=get_page_split(sample,page_info) - - result_all[element]={ - 'all':result, - 'group':group_result, - 'page':page_result - } - - dict_list = [] - save_dict={} - en_overall=[] - ch_overall=[] - for category_type, metric in [("text_block", "Edit_dist"), ("display_formula", "Edit_dist"), ("display_formula", "CDM"), ("table", "TEDS"), ("table", "Edit_dist"), ("reading_order", "Edit_dist")]: - if metric == 'CDM': - save_dict[category_type+'_'+metric+'_EN'] = '-' - save_dict[category_type+'_'+metric+'_CH'] = '-' - elif metric == "TEDS": - save_dict[category_type+'_'+metric+'_EN'] = result_all[category_type]["page"][metric]["language: english"] * 100 - save_dict[category_type+'_'+metric+'_CH'] = result_all[category_type]["page"][metric]["language: simplified_chinese"] * 100 - else: - save_dict[category_type+'_'+metric+'_EN'] = result_all[category_type]["page"][metric].get("language: english", np.nan) - save_dict[category_type+'_'+metric+'_CH'] = result_all[category_type]["page"][metric].get("language: simplified_chinese",np.nan) - if metric == "Edit_dist": - en_overall.append(result_all[category_type]["page"][metric].get("language: english", np.nan)) - ch_overall.append(result_all[category_type]["page"][metric].get("language: simplified_chinese",np.nan)) - - save_dict['overall_EN'] = sum(en_overall) / len(en_overall) - save_dict['overall_CH'] = sum(ch_overall) / len(ch_overall) - dict_list.append(save_dict) - df = pd.DataFrame(dict_list,index=['end2end',]).round(3) - - return df - - - -eval=Omnidocbenchend2endEvaluator(eval_file,tsv_path) -result=eval.score() -print(result) - - - - - - - - - - - - - - - - - - - - -# print(result['overall_CH']) - -#sample -""" -{'gt_idx': [0], -'gt': '\n\n\n \n\n\n\n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n
九毛九国际控股有限公司(09922)
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600_ _
', -'pred_idx': [''], -'pred': '', -'gt_position': [4], -'pred_position': '', -'norm_gt': '
九毛九国际控股有限公司(09922)
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600_ _
', -'norm_pred': '', -'gt_category_type': 'table', -'pred_category_type': '', -'gt_attribute': [{'table_layout': 'horizontal', 'with_span': True, 'line': 'wireless_line', 'language': 'table_simplified_chinese', 'include_equation': False, 'include_photo': False, 'include_background': True, 'with_structured_text': False}], 'edit': 1, -'img_id': 'yanbaopptmerge_70a45bf10fb90ae0c91d44a11e0a97a8f9515d85626b54eb50a80b6146e5c2bf.pdf_10.jpg', -'metric': {'TEDS': 0.0, 'TEDS_structure_only': 0.0}} - - -{'gt_idx': [0], -'gt': '\n\n\n \n\n\n\n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n
九毛九国际控股有限公司(09922)
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600_ _
', -'norm_gt': '
九毛九国际控股有限公司(09922)
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600_ _
', -'gt_category_type': 'table', -'gt_position': [4], -'gt_attribute': [{'table_layout': 'horizontal', 'with_span': True, 'line': 'wireless_line', 'language': 'table_simplified_chinese', 'include_equation': False, 'include_photo': False, 'include_background': True, 'with_structured_text': False}], -'pred_idx': [0], -'pred': '\n \n \n \n \n \n \n \n \n \n \n \n \n
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600--
', -'norm_pred': '
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600--
', -'pred_category_type': '', -'pred_position': '', -'edit': 0.6423529411764706, -'img_id': 'yanbaopptmerge_70a45bf10fb90ae0c91d44a11e0a97a8f9515d85626b54eb50a80b6146e5c2bf.pdf_10.jpg'} - -""" - - - - - - - - - - - - - - -""" - -{'gt_idx': [4], -'gt': '[14]1:https://www.163.com/...\n[15]1:https://www.163.com/... 2:https://www.dianping.... 3：烤鱼行业品牌竞争分析.\n[16] 1：烤鱼行业竞争格局维度…．\n[17] 1:https://aiqicha.baidu.c... 2:上市公司信息', - 'pred_idx': [7], -'pred': '[14] 1：https://www.163.com/...\n[15] 1：https://www.163.com/...\n[16] 1：https://www.dianping...\n[17] 1：https://aiqicha.baidu.com...\n2：上市公司信息', -'edit': 0.2815533980582524, -'gt_position': [5], -'pred_position': 540, -'norm_gt': '141httpswww163com151httpswww163com2httpswwwdianping3烤鱼行业品牌竞争分析161烤鱼行业竞争格局维度171httpsaiqichabaiduc2上市公司信息', -'norm_pred': '141httpswww163com151httpswww163com161httpswwwdianping171httpsaiqichabaiducom2上市公司信息', -'gt_category_type': 'text_block', -'pred_category_type': 'text_block', -'gt_attribute': [{'text_language': 'text_en_ch_mixed', 'text_background': 'single_colored', 'text_rotate': 'normal'}], -'img_id': 'yanbaopptmerge_70a45bf10fb90ae0c91d44a11e0a97a8f9515d85626b54eb50a80b6146e5c2bf.pdf_10.jpg'}, - -""" - -""" -gt_table_list: -[ -{'category_type': 'table', -'poly': [98.88907451996393, 485.43740433673287, 706.4349152824724, 485.43740433673287, 706.4349152824724, 634.749517744468, 98.88907451996393, 634.749517744468], -'ignore': False, -'order': 4, -'anno_id': 5, -'latex': '\\begin{tabular}{|l|l|l|l|} \\hline\n\\multicolumn{4}{l}{九毛九国际控股有限公司(09922)} \\\\ \\hline\n总市值 & 营收规模 & 同比增长(\\%) & 毛利率(\\%) \\\\ \\hline\n88.0亿 & 41.8亿 & 53.9600 & \\_ \\_ \\hline\\\\ \\hline\n\\end{tabular}', -'html': '\n\n\n \n\n\n\n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n
九毛九国际控股有限公司(09922)
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600_ _
', -'attribute': {'table_layout': 'horizontal', 'with_span': True, 'line': 'wireless_line', 'language': 'table_simplified_chinese', 'include_equation': False, 'include_photo': False, 'include_background': True, 'with_structured_text': False}, -'table_edit_status': 'good'}, -{'category_type': 'table', 'poly': [74.32924890663881, 1433.6914006928678, 1553.1167323978748, 1433.6914006928678, 1553.1167323978748, 1933.505770406509, 74.32924890663881, 1933.505770406509], 'ignore': False, 'order': 12, 'anno_id': 10, 'latex': '\\begin{tabular}{|l|l|l|l|} \\hline\n\\multicolumn{4}{c}{·公司信息} \\\\ \\hline\n企业状态 & 存续 & 注册资本 & 13500万人民币 \\\\ \\hline\n企业总部 & 市辖区 & 行业 & 商务服务业 \\\\ \\hline\n法人 & 陈文豪 & 统一社会信用代码 & 91110000101443599G \\\\ \\hline\n企业类型 & 有限责任公司（台港澳法人独资） & 成立时间 & 1995-09-29 \\\\ \\hline\n品牌名称 & \\multicolumn{3}{l}{美诺（北京）餐饮管理有限公司} \\\\ \\hline\n经营范围 & \\multicolumn{3}{l}{代理记帐；以下项目限分支机构经营：餐饮服务（含凉菜、不含裱花蛋糕、不含生食水海产...查看更多}\\\\ \\hline\n\\end{tabular}\n', 'html': '\n\n\n \n\n\n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n \n \n\n\n \n \n\n\n \n \n\n\n
·公司信息
企业状态存续注册资本13500万人民币
企业总部市辖区行业商务服务业
法人陈文豪统一社会信用代码91110000101443599G
企业类型有限责任公司（台港澳法人独资）成立时间1995-09-29
品牌名称美诺（北京）餐饮管理有限公司
经营范围代理记帐；以下项目限分支机构经营：餐饮服务（含凉菜、不含裱花蛋糕、不含生食水海产...查看更多
', -'attribute': {'table_layout': 'horizontal', 'with_span': True, 'line': 'full_line', 'language': 'table_simplified_chinese', 'include_equation': False, 'include_photo': False, 'include_background': True, 'with_structured_text': False}, -'table_edit_status': 'good'} -] -""" - -""" -VLME - -pred_content:(51,207),(476,257) - -横纵名为门店数量，纵轴为毛利率。 - -(51,271),(476,321) - -上市公司速览 - -(51,335),(476,385) - -九毛九国际控股有限公司 (09922) - -| 总市值 | 营收规模 | 同比增长(%) | 毛利率(%) | -| :--: | :--: | :--: | :--: | -| 88.0亿 | 41.8亿 | 53.9600 | -- | - -(51,409),(476,459) - -[14] 1：https://www.163.com/... -[15] 1：https://www.163.com/... -[16] 1：https://www.dianping... -[17] 1：https://aiqicha.baidu.com... -2：上市公司信息 - -(51,513),(476,563) - -烤鱼代表企业分析 - -(51,587),(928,817) - -美诺（北京）餐饮管理有限公司[18] - -| 公司信息 | -| :--: | -| 企业状态 | 存续 | -| 企业总部 | 市辖区 | -| 法人 | 陈文豪 | -| 企业类型 | 有限责任公司(台港澳法人独资) | -| 品牌名称 | 美诺（北京）餐饮管理有限公司 | -| 经营范围 | 代理记帐；以下项目限分支机构经营：餐饮服务（含凉菜、不含裱花蛋糕、不含生食水海产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花 - - -pred_dataset:defaultdict(, -{'text_all': [ -{'category_type': 'text_all', 'position': [0, 18], 'content': '(51,207),(476,257)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [18, 34], 'content': '横纵名为门店数量，纵轴为毛利率。', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [34, 52], 'content': '(51,271),(476,321)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [52, 58], 'content': '上市公司速览', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [58, 76], 'content': '(51,335),(476,385)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [76, 95], 'content': '九毛九国际控股有限公司 (09922)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [95, 191], 'content': '| 总市值 | 营收规模 | 同比增长(%) | 毛利率(%) |\n| :--: | :--: | :--: | :--: |\n| 88.0亿 | 41.8亿 | 53.9600 | -- |', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [191, 209], 'content': '(51,409),(476,459)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [209, 346], 'content': '[14] 1：https://www.163.com/...\n[15] 1：https://www.163.com/...\n[16] 1：https://www.dianping...\n[17] 1：https://aiqicha.baidu.com...\n2：上市公司信息', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [346, 364], 'content': '(51,513),(476,563)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [364, 372], 'content': '烤鱼代表企业分析', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [372, 390], 'content': '(51,587),(928,817)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [390, 408], 'content': '美诺（北京）餐饮管理有限公司[18]', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [408, 2811], 'content': '| 公司信息 |\n| :--: |\n| 企业状态 | 存续 |\n| 企业总部 | 市辖区 |\n| 法人 | 陈文豪 |\n| 企业类型 | 有限责任公司(台港澳法人独资) |\n| 品牌名称 | 美诺（北京）餐饮管理有限公司 |\n| 经营范围 | 代理记帐；以下项目限分支机构经营：餐饮服务（含凉菜、不含裱花蛋糕、不含生食水海产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花', 'fine_category_type': 'text_block'} -] - }) - -""" - -""" -omnidoc - - -pred_content:(51,207),(476,257) - -横纵名为门店数量，纵轴为毛利率。 - -(51,271),(476,321) - -上市公司速览 - -(51,335),(476,385) - -九毛九国际控股有限公司 (09922) - -| 总市值 | 营收规模 | 同比增长(%) | 毛利率(%) | -| :--: | :--: | :--: | :--: | -| 88.0亿 | 41.8亿 | 53.9600 | -- | - -(51,409),(476,459) - -[14] 1：https://www.163.com/... -[15] 1：https://www.163.com/... -[16] 1：https://www.dianping... -[17] 1：https://aiqicha.baidu.com... -2：上市公司信息 - -(51,513),(476,563) - -烤鱼代表企业分析 - -(51,587),(928,817) - -美诺（北京）餐饮管理有限公司[18] - -| 公司信息 | -| :--: | -| 企业状态 | 存续 | -| 企业总部 | 市辖区 | -| 法人 | 陈文豪 | -| 企业类型 | 有限责任公司(台港澳法人独资) | -| 品牌名称 | 美诺（北京）餐饮管理有限公司 | -| 经营范围 | 代理记帐；以下项目限分支机构经营：餐饮服务（含凉菜、不含裱花蛋糕、不含生食水海产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花 - -pred_dataset:defaultdict(, -{'text_all': [{'category_type': 'text_all', 'position': [0, 19], 'content': '(51,207),(476,257)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [19, 35], 'content': '横纵名为门店数量，纵轴为毛利率。', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [35, 53], 'content': '(51,271),(476,321)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [53, 59], 'content': '上市公司速览', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [59, 77], 'content': '(51,335),(476,385)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [77, 96], 'content': '九毛九国际控股有限公司 (09922)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [522, 540], 'content': '(51,409),(476,459)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [540, 677], 'content': '[14] 1：https://www.163.com/...\n[15] 1：https://www.163.com/...\n[16] 1：https://www.dianping...\n[17] 1：https://aiqicha.baidu.com...\n2：上市公司信息', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [677, 695], 'content': '(51,513),(476,563)', 'fine_category_type': 'text_block'}, -{'category_type': 'text_all', 'position': [695, 703], 'content': '烤鱼代表企业分析', 'fine_category_type': 'text_block'}, - {'category_type': 'text_all', 'position': [703, 721], 'content': '(51,587),(928,817)', 'fine_category_type': 'text_block'}, - {'category_type': 'text_all', 'position': [721, 739], 'content': '美诺（北京）餐饮管理有限公司[18]', 'fine_category_type': 'text_block'}, - {'category_type': 'text_all', 'position': [1381, 3672], 'content': '| 经营范围 | 代理记帐；以下项目限分支机构经营：餐饮服务（含凉菜、不含裱花蛋糕、不含生食水海产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花蛋糕、不含生食水产品、不含裱花', 'fine_category_type': 'text_block'} - ], -'html_table': [{'category_type': 'html_table', 'position': [109, 534], 'content': '\n \n \n \n \n \n \n \n \n \n \n \n \n
总市值营收规模同比增长(%)毛利率(%)
88.0亿41.8亿53.9600--
', 'fine_category_type': 'md2html_table'}, {'category_type': 'html_table', 'position': [768, 1409], 'content': '\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
公司信息
企业状态存续
企业总部市辖区
法人陈文豪
企业类型有限责任公司(台港澳法人独资)
品牌名称美诺（北京）餐饮管理有限公司
', 'fine_category_type': 'md2html_table'}]}) -""" \ No newline at end of file